The problem of allocating multiple emergency service resources to protect critical transportation infrastructures is studied in this paper. Different modeling approaches, including deterministic, stochastic programming, and robust optimization, are used to model various risk preferences in decision making under uncertain service availability and accessibility. Singapore is used as a case study for numerical experiments. The performances of different models are compared in terms of allocation strategies and the reliability and robustness of the system.